Optimizing Biosensor Measurements for Multicomponent Reactions

由于表面等离子体共振（SPR）和类似的生物传感器通常仅测量质量变化，因此可能难以分析来自多个同时反应的数据。 该项目考虑了跨损伤DNA合成中聚合酶转换的例子，这对于细胞科普DNA损伤至关重要。 聚合酶转换过程涉及多个组分。 一个“工具带”模型已被提出的开关，但试图验证模型与传统的荧光镜检查修改的蛋白质的正常活动。 因此，无标记的技术，如SPR是可取的，但需要更复杂的数学建模。 将产生将传感图信号与潜在动力学相关联的数学模型。 该模型的渐近分析和数值模拟将被用来设计一组实验，可以确定一般系统的动力学途径。 这些结果将用于测试“工具带”模型。 特别是，通过调整各种物质的相对浓度，人们可以进入少数反应占主导地位的状态。 在研究疾病和其他生物系统的基础过程时，生物化学家经常需要精确地估计反应速率。 光学生物传感器是测量这种反应的一种流行方法，因为它们不会干扰底层系统。 正确解释生物传感器数据需要系统的精确数学模型。 当多个反应同时发生时，当前模型不能很好地工作。 将开发一个新的模型，为科学家提供如何设计实验以分析同时发生的多个反应的一般路线图。 特别是，数学将被用来验证一个模型来解释translesion DNA合成，这是细胞科普DNA损伤所必需的。

Since surface plasmon resonance (SPR) and similar biosensors typically measure only mass changes, it can be difficult to analyze data from multiple simultaneous reactions. This project considers the example of polymerase switching in translesion DNA synthesis, which is essential for cells to cope with DNA damage. The polymerase switch process involves multiple components. A "tool-belt" model has been proposed for the switch, but attempting to verify the model with conventional fluoroscopy modifies the normal activity of the proteins. Thus, a label-free technique such as SPR is desirable, but requires more sophisticated mathematical modeling. A mathematical model will be produced that relates the sensogram signal to the underlying kinetics. Asymptotic analysis and numerical simulations of this model will be used to design a set of experiments that can determine the kinetic pathway for a general system. These results will be used to test the "tool-belt" model. In particular, by adjusting the relative concentrations of the various species, one can move into regimes where a few reactions dominate. Then the rate constants can be easily ascertained, since the nonlinear governing equations will be simplified.When studying the processes underpinning disease and other biological systems, biochemists often need accurate estimates of reaction rates. Optical biosensors are a popular way to measure such reactions, as they do not disturb the underlying system. Correctly interpreting biosensor data requires having an accurate mathematical model for the system. Current models do not work well when multiple reactions are occurring simultaneously. A new model will be developed that will provide scientists a general roadmap on how to design experiments to analyze multiple reactions that occur at the same time. In particular, the mathematics will be used to validate a model to explain translesion DNA synthesis, which is essential for cells to cope with DNA damage.